- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Ratio-Based Multitemporal SAR Images Denoising: RABASAR
摘要： In this paper, we propose a fast and efficient multitemporal despeckling method. The key idea of the proposed approach is the use of the ratio image, provided by the ratio between an image and the temporal mean of the stack. This ratio image is easier to denoise than a single image thanks to its improved stationarity. Besides, temporally stable thin structures are well preserved thanks to the multitemporal mean. The proposed approach can be divided into three steps: 1) estimation of a 'superimage' by temporal averaging and possibly spatial denoising; 2) denoising of the ratio between the noisy image of interest and the 'superimage'; and 3) computation of the denoised image by remultiplying the denoised ratio by the 'superimage.' Because of the improved spatial stationarity of the ratio images, denoising these ratio images with a speckle-reduction method is more effective than denoising images from the original multitemporal stack. The amount of data that is jointly processed is also reduced compared to other methods through the use of the 'superimage' that sums up the temporal stack. The comparison with several state-of-the-art reference methods shows better results numerically (peak signal-noise-ratio and structure similarity index) as well as visually on simulated and synthetic aperture radar (SAR) time series. The proposed ratio-based denoising framework successfully extends single-image SAR denoising methods to time series by exploiting the persistence of many geometrical structures.
关键词： ratio image,speckle reduction,Multitemporal synthetic aperture radar (SAR) series,superimage
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Significant Wave Height Retrieval from Gaofen-3 Wave Mode Images
摘要： Significant wave height (Hs), is an important parameter, represented as the integration of directional wave spectra. Although many researchers have directly extracted Hs from SAR images and got a great accuracy of retrieval, those approaches are not suitable for GF-3 SAR data. In this paper, we propose an empirical approach for SAR Hs retrieval, using λc estimated from the real part of image cross spectra obtain from VV-polarized Gaofen-3 (GF-3) wave mode data acquired in different radar beams (called wave-code). Results using GF-3 wave mode data from January to February 2017 indicate that the bias and RMSE errors are: 189 wave-code, -0.13 m and 0.57 m; 190 wave-code, -0.07 m and 0.34 m; 193 wave-code, -0.3 m and 0.59 m; 199 wave-code, 0.16 m and 0.68 m; 215 wave-code, 0.2 m and 0.87 m. they show a relative behavior between the retrieved Hs and the Hs extracted from WAVEWATCH-III (WW3). However, there is a significant error when WW3-extracted Hs exceed 4 m. It seems that the model is not suitable for Hs retrieval on high sea conditions.
关键词： wave empirical retrieval,GF-3,cutoff wavelength estimation,synthetic aperture radar,wave mode
更新于2025-09-23 15:22:29
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Sea Clutter Cancellation for Passive Radar Sensor Exploiting Multi-Channel Adaptive Filters
摘要： Sea clutter suppression in passive radar sensor is a challenging problem because the Doppler frequencies of low-velocity sea-surface targets are typically close to the spectrum of the sea clutter. Conventional approaches based on single-channel high-pass filters are effective for clutter suppression only when the clutter is concentrated in low Doppler region. For sea clutter that has a spread spectrum, however, these approaches have to compromise target signal reception. That is, they either form a narrow notch which does not effectively suppress clutter, or generate a broadened null that simultaneously mitigates low-velocity target signals. Therefore, it is desirable to design a filter that forms a notch broad enough to cover the entire clutter spectrum, with the frequency response rising sharply to a high gain outside the clutter band. Toward this end, in this paper, we develop a generalized multi-channel adaptive filter which, by forming multiple sharp notches over a set of discrete frequencies within the clutter spectrum, achieves effective clutter suppression and target signal preservation. We focus on the fast frequency-domain implementation, and the performance analysis in terms of the frequency response, signal energy loss, and computational complexities is also presented. The effectiveness of the proposed approaches is verified using real-data results.
关键词： interference cancellation,sea clutter,multi-channel adaptive filter,Passive radar sensor,normalized least mean square (NLMS)
更新于2025-09-23 15:22:29
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An Online Multiview Learning Algorithm for PolSAR Data Real-Time Classification
摘要： Polarimetric synthetic aperture radar (PolSAR) data are sequentially acquired and usually large scale. Fast and accurate classification is particularly important for their applications. By introducing online learning, the PolSAR system can learn a classification model incrementally from a stream of instances, which is of high efficiency for newly arrived samples processing, strong adaptability for a dynamically changing environment, and excellent scalability for rapidly increasing data. In this paper, we propose an Online Multi-view Passive-Aggressive learning algorithm, named OMPA, for PolSAR data real-time classification. The polarimetric, color, and texture features are extracted to characterize PolSAR data, and each type of features corresponds to one view. In order to exploit the consistency and complementary property of these views, we give a new optimization model that ensembles the classifiers of multiple distinct views and enforces the agreement between each predictor and the combined predictor. The corresponding algorithms for both binary and multiclass classification tasks are derived, and the update steps have analytical solutions. In addition, we rigorously derive a bound on the number of prediction mistakes of the method. The proposed OMPA algorithm is evaluated on two real PolSAR datasets for built-up areas extraction and land cover classification, respectively. Experimental results demonstrate that OMPA consistently maintains a smaller mistake rate with low time cost and achieves about 1% and 2% accuracy improvements on the datasets, respectively, compared with the best results of the previously known online single-view and multiview learning methods.
关键词： polarimetric synthetic aperture radar (PolSAR),Multiview learning,passive-aggressive (PA) algorithm,online classification
更新于2025-09-23 15:22:29
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PolSAR Coherency Matrix Optimization Through Selective Unitary Rotations for Model-Based Decomposition Scheme
摘要： In this letter, a special unitary SU(3) matrix group is exploited for coherency matrix transformations to decouple the energy between orthogonal states of polarization. This decoupling results in the minimization of the cross-polarization power along with the removal of some off-diagonal terms of coherency matrix. The proposed unitary transformations are utilized on the basis of the underlying dominant scattering mechanism. By doing so, the reduced power from the cross-polarization channel is always concentrated on the underlying dominant co-polar scattering component. This makes it unique in comparison to state-of-the-art techniques. The proposed methodology can be adopted to optimize the coherency matrix to be used for the model-based decomposition methods. To verify this, pioneer three-component decomposition model is implemented using the proposed optimized coherency matrix of two different test sites. The comparative studies are analyzed to show the improvements over state-of-the-art techniques.
关键词： Coherency matrix,polarimetric synthetic aperture radar (PolSAR),cross-polarization,unitary matrix rotation,land-cover classification
更新于2025-09-23 15:22:29
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[IEEE 2018 International Conference on Radar (RADAR) - Brisbane, Australia (2018.8.27-2018.8.31)] 2018 International Conference on Radar (RADAR) - High-Resolution Radar Imaging with Unknown Noise
摘要： This paper addresses the problem of high-resolution radar imaging in complex environments with unknown noise in a Bayesian framework. In the new statistical model, the noise obeys Gaussian mixture distribution, and the weights are governed by the sparsity-promoting Gamma-Gaussian hierarchical prior. Then, the weights are estimated via the maximum a posterior-expectation maximization (MAP-EM) technique. Experiments have shown that the proposed method provides an effective way of high-resolution radar imaging in complex environments such as barrage jamming.
关键词： Radar imaging,jamming suppression,Bayesian learning,MAP-EM,GMM
更新于2025-09-23 15:22:29
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[IEEE 2018 International Conference on Radar (RADAR) - Brisbane, Australia (2018.8.27-2018.8.31)] 2018 International Conference on Radar (RADAR) - Radar Cross Section of Modified Target Using Gaussian Beam Methods: Experimental Validation
摘要： The aim of this paper is to study the Radar Cross Section (RCS) of modified radar targets (plate with notch) using Gaussian Beam techniques. The Gaussian methods used in this work are Gaussian Beam Summation (GBS) and Gaussian Beam Launching (GBL). We establish the theoretical formulation of the GBS and GBL techniques and analyze the influence of the main Gaussian beam parameters on the variation of the scattered field. Then, we present the simulations of RCS. The numerical results are compared with PO, MoM methods, and also with experimental measurements performed in the anechoic chamber at Lab-STICC (ENSTA Bretagne).
关键词： Radar Cross Section (RCS),Physical Theory of Diffraction (PTD),Physical Optic (PO),Gaussian Beam Summation (GBS),Gaussian Beam Launching(GBL),Method of Moment (MoM)
更新于2025-09-23 15:22:29
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[IEEE 2018 International Conference on Radar (RADAR) - Brisbane, Australia (2018.8.27-2018.8.31)] 2018 International Conference on Radar (RADAR) - Space-Range-Doppler Focus Processing: A Novel Solution for Moving Target Integration and Estimation Using FDA-MIMO Radar
摘要： Frequency diverse array (FDA) is an emerging array technique that employs a small frequency increment across its array elements to produce a range-angle-dependent beampattern, which provides promising applications for joint angle-range estimation of targets. However, the FDA has several problems for signal processing, such as angle and range coupling and Doppler integration, and few papers deal with FDA for moving target. In this paper, we combine the FDA and MIMO technique and propose the concept of space (angle)-range-Doppler (SRD) focus processing, which is a novel solution for moving target integration and estimation. It utilizes the property of FDA and high-resolution Doppler processing of MIMO. Based on the data model of FDA-MIMO radar, we provide a practical method for SRD processing via A&D joint estimation and sparse time-frequency distribution (STFD). Both theoretical and numerical simulation results verify that proposed method has better ability for joint range-angle-Doppler processing, which can be used for low-observable moving target detection and estimation under complex environment.
关键词： Moving target integration,Space-Range-Doppler (SRD) focus processing,Frequency diverse array (FDA) radar,Sparse time-frequency distribution (STFD)
更新于2025-09-23 15:22:29
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[IEEE 2018 China International SAR Symposium (CISS) - Shanghai (2018.10.10-2018.10.12)] 2018 China International SAR Symposium (CISS) - Reconstruction Full-Pol SAR Data from Single-Pol SAR Image Using Deep Neural Network
摘要： Compared with single channel polarimetric (single-pol) SAR image, full polarimetric (full-pol) data convey richer information, but with compromises on higher system complexity and lower resolution or swath. In order to balance these factors, a deep neural networks based method is proposed to recover full-pol data from single-pol data in this paper. It consists of two parts: a feature extractor network is applied first to extract hierarchical multi-scale spatial features, followed by a feature translator network to predict polarimetric features with which full-pol SAR data can be recovered. Both qualitative and quantitative results show that the recovered full-pol SAR data agrees well with the real full-pol data. No prior information is assumed for scatterer media, and the framework can be easily expanded to recovery full-pol data from non-full-pol data. Traditional PolSAR applications such as model-based decomposition and unsupervised classification can now be applied directly to recovered full-pol SAR image to interpret the physical scattering mechanism.
关键词： synthetic aperture radar (SAR),deep neural network (DNN),polarimetric reconstruction
更新于2025-09-23 15:22:29
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[IEEE 2018 China International SAR Symposium (CISS) - Shanghai (2018.10.10-2018.10.12)] 2018 China International SAR Symposium (CISS) - A High Frequency Vibration Compensation Approach in Terahertz SAR Based on Wavelet Multi-Resolution Analysis
摘要： The use of terahertz wave in SAR imaging can solve the difficulties of frame rate and detection of slow moving targets in conventional SAR imaging. The most important difference between terahertz SAR (THz-SAR) and conventional SAR is the treatment on motion compensation. For reason that the wavelength of terahertz SAR is much shorter than that of conventional microwave SAR, the tiny vibration of SAR platform will blur SAR images, especially high frequency components. The high frequency vibration will result in paired echoes in SAR imaging, which can't be focused with traditional SAR imaging algorithms. Thus the vibration parameters can't be estimated precisely enough to construct the reference function to compensate the sinusoidal modulation phase. So we first get focused paired echoes in terahertz SAR imaging through Doppler keystone transform (DKT), then we propose a frequency estimation method based on wavelet multi-resolution analysis, along with parametric space projection method, to complete the high frequency vibration estimation of terahertz SAR. At last, the numerical tests using the point target echoes validate the proposed method.
关键词： synthetic aperture radar,high frequency vibration error,Terahertz,wavelet multi-resolution analysis,vibration estimation
更新于2025-09-23 15:22:29